Supply chain professionals need uncanny reflexes. The moment they get a handle on raw materials, labor expenses, international legislation and shipping conditions, the ground shifts beneath them, and all the effort they put into pushing their boulder up the hill comes undone.
With the global nature of today’s supply chain environment, the factors governing a company’s bottom line are highly unpredictable. The solution lies in predictive analytics for supply chain management.
This particular branch of analytics offers an opportunity for organizations to anticipate challenges before they happen. Yet just 30% of supply chain professionals are using their data to forecast the future.
Following are some competitive advantages that companies are missing when they choose to ignore predictive operational analytics.
Enhanced Demand Forecasting
Supply chain leaders today are faced with an increasing array of expected and unexpected sales drivers. Though traditional demand forecasting yields some insight from a single variable or small dataset, real-world supply chain forecasting requires tools that are capable of anticipating demand based on a messy, multifaceted assembly of key motivators. Otherwise, they risk regular profit losses as a result of the bullwhip effect, buying far more products or raw materials than are necessary.
One international manufacturer was struggling to make accurate predictions about future demand using traditional forecasting models. Its dependence on historical sales data of individual SKUs was resulting in longer order lead times, and lack of insight into seasonal trends was leading to lost profits. By implementing machine learning models and statistical packages, the company was able to evaluate the impact of various influencers on the demand of each product. In the process, it saw an 8% increase in weekly demand forecast accuracy and 12% increase in monthly demand forecast accuracy.
This practice can be carried across the supply chain in any organization, whether demand is relatively predictable with minor spikes or inordinately complex. The right predictive analytics platform can clarify the patterns and motivations behind complex systems, and help create a steady supply of products without expensive surpluses.
Smarter Risk Management
The modern supply chain is a precise yet delicate machine. The procurement of raw materials and components from a decentralized and global network has the potential to cut costs and increase efficiencies — as long as the entire process is operating perfectly. Any type of disruption or bottleneck in the supply chain can create a massive liability, threatening both customer satisfaction and the bottom line. When organizations leave their fate up to reactive risk management practices, these disruptions are especially steep.
Predictive risk management allows organizations to audit each component or process within the supply chain for its potential to destabilize operations. For example, if a company currently imports raw materials such as copper from Chile, predictive risk management would account for the threat of common Chilean natural disasters such as flooding or earthquakes. That same logic applies to any country or point of origin for raw materials.
Companies can evaluate the cost and processes of normal operations and the ways in which new potentialities would impact your business. Though you can’t prepare for every possible event, you can have contingencies in place to mitigate losses and maintain supply chain flow.
Formalized Process Improvement
As with any industry facing internal and external pressures to pioneer new efficiencies, the supply chain industry can’t rely on happenstance to evolve. There needs to be a twofold solution: one, a culture of continuous organizational improvement across the business, and two, tools for identifying opportunities and taking meaningful action.
For the second part, one of the most effective tools is predictive analytics for supply chain management. Machine learning algorithms are exceptional at unearthing inefficiencies or bottlenecks, giving stakeholders the ability to make informed decisions. Because predictive analytics removes most of the grunt work and exploration associated with process improvement, it’s easier to create a standardized system of seeking out greater efficiencies. Finding new improvements is almost automatic.
Ordering is an area that offers plenty of opportunities for improvement. If there’s an established relationship with an individual customer (be it retailer, wholesaler, distributor or the direct consumer), an organization has stockpiles of information on individual and demographic customer behavior. It can in turn be deployed alongside internal and third-party data sources to anticipate product orders before they’re made. This type of ordering can accelerate revenue generation, increase customer satisfaction, and streamline shipping and marketing costs.
Ryan Lewis is managing consultant for data insights at 2nd Watch.
Timely, incisive articles delivered directly to your inbox.